Distributed Estimation Fusion Using the State Prediction-based Adaptive Consensus Filter

This paper utilizes the adaptive filter to improve the consensus filter-based estimation fusion algorithm for accelerating the convergence of the nodes' estimates,and proposes the state prediction-based adaptive consensus filter.In this algorithm,each node uses the predicted state as a reference signal for the adaptive filter,and modifies the weighted matrix of the consensus filter according to the algorithm of the adaptive filter.The simulation results demonstrate that the proposed algorithm can not only accelerate the convergence of the nodes' estimates,but also decrease the estimation error before the convergence is reached.